Industrial Process Modelling and Control

1. Industrial Process Modelling and Control Ton Backx EmeritaatsvieringJoosVandewalle

2. Outline History Process performance and process control Model predictive control essentials Process modeling Current developments Future perspective Emeritaatsviering Joos Vandewalle

3. Model Predictive Control History Early developments of Model Predictive Control (MPC) technology were initiated by two pioneers: • Dr. Jacques Richalet (Adersa, 1976) • ‘Model Predictive Heuristic Control’ (MPHC) using IDCOM as the MPC software for process identification (IDentification) and for control (COMmand) • Use of Finite Impulse Response (FIR) models • Control inputs computed by minimization of a finite horizon quadratic objective function without consideration of constraints • Plant output behavior specified by reference trajectories Emeritaatsviering Joos Vandewalle

4. Model Predictive Control History (cont’d) • Dr. Charles Cutler (Shell Oil, 1979) • ‘Dynamic Matrix Control’ (DMC) • Use of Finite Step Response (FSR) model • Linear objective function subject to linear inequality constraints using a finite prediction horizon (LP) • Plant output behavior specified by setpoints • Optimum inputs calculated by solving a Linear Programming problem Emeritaatsviering Joos Vandewalle

5. Process performance and process control disturbances Process manipulated variables controlled variables Process performance is governed by: • Critical process and product variables –”Controlled Variables”- need to meet specifications • During startup, shut-down and product changeovers off-spec products are produced • Need for minimization of transition losses • During production disturbances cause variations in critical variables • Need for disturbance rejection Emeritaatsviering Joos Vandewalle

6. Process performance and process control Operating information Costs and Specifications • optimum operating conditions are determined by an optimizer (setpoints, set ranges, priorities and weights, operating constraints) Optimizer Operating information Targets (setpoints, setranges, …) Operating information Targets (setpoints, setranges, …) • the model predictive control system realizes targets set by the optimizer MPC Process values setpoints PID Process Model predictive control is the supervisory control layer that enables process optimization by minimization of production costs ensuring product specifications and production quantities Emeritaatsviering Joos Vandewalle

7. Process performance and process control Measured process signal 21 Cpk = 0.96 Cpk = 4.3 20.8 Cpk = 0.96 Cpk = 1.6 Cpk = 0.96 20.6 20.4 20.2 probability density function Economic benefit value 20 19.8 19.6 19.4 8 6 4 2 0 12 10 8 6 4 2 0 12 10 19.2 2 1 0 3 19 2.5 1.5 0.5 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 time 4 probability density x 10 Visualization of benefit realization by MPC Standard Control Model Predictive Control with performance optimization Model Predictive Control without optimization Emeritaatsviering Joos Vandewalle

8. Model predictive control essentials disturbances manipulated variables controlled variables Unit Process f g Operating Constraints Disturbance Model measured disturbances Process Model Process Model Process Model + - Setpoints Set ranges Controller Optimization and constraint handling Model Predictive Controller MPC strength is based on the explicit use of (a) (set of) model(s): • to predict future process output behavior • to determine the best future input manipulations to drive the process to optimum conditions • to feedforward compensate disturbances • to respect operating constraints and to determine optimum conditions • To handle non-linearities

9. Model predictive control essentials Past control manipulations Future control manipulations Control horizon Predicted future process responses Setpoint value Past process responses Prediction horizon Dead time Output horizon applied for optimization Time (t) Time (t) Past Future Present moment Emeritaatsviering Joos Vandewalle

10. Model predictive control essentials Past Future Past Future Cannot be influenced any more Still to be determined by future inputs • In this expression: • Yfp denotes the part of the future outputs stemming from past input manipulations • Yff denotes the part of the future outputs resulting from future input manipulations Linear models are used to calculate the responses to past and future process input manipulations and similarly to predict future responses to known disturbances Emeritaatsviering Joos Vandewalle

11. Process modeling Process application example Emeritaatsviering Joos Vandewalle

12. Process modeling Model-based automation applications for decision support Operator training Detailed design and optimisation of process equipment Process flowsheeting Laboratory experiment design and optimisation Model Predictive control Equipment performance monitoring Process Health monitoring E V O L V I N G M A S T E R M O D E L Troubleshooting with detailed predictivemodels OPERATION CONCEPT DESIGN New process design Simultaneous equipmentand controldesign andoptimisation DESIGN Pn + M  Pn+1 …. Detailed design of complex units Design of optimal operating procedures Emeritaatsviering Joos Vandewalle

13. Process modeling System identification is the modeling technique applied in industry for sufficiently accurate modeling of the relevant process dynamics for MPC • Data driven modeling • Model set: Non-parametric, semi-parametric, parametric • Model structure • Parameter estimation criterion: Output error, equation error, input error Emeritaatsviering Joos Vandewalle

14. Process modeling Required capabilities of models • Accuracyon-line assessment of model validity • Adaptabilityflexible on-line updating of models (dynamics and interconnection structure) • Active data-driven learningdemands on accuracy, autonomy, robustness active probing for information Emeritaatsviering Joos Vandewalle

15. Process modeling Example of current limitations: • MPC projects in industry highly depend on accurate plant models and well-tuned controllers • Controllers and models are verified (identified) during commissioning • When during operation process behavior changes: MPC’s are switched to “manual” • Loss of performance • Expensive experimental campaign to re-identify the models is the only way out Emeritaatsviering Joos Vandewalle

16. Back to the core of the problem of data-driven modeling / identification of Linear Time Invariant (LTI) models Process modeling Emeritaatsviering Joos Vandewalle

17. Process modeling The classical identification problems: open loop closed loop Identify a plant model on the basis of measured signalsu,y (and possiblyr) • Several classical methods available (Prediction Error, subspace, Output Error, non-parametric,..) • Well known results for identification in known structure(open loop, closed-loop, possibly known controller) Emeritaatsviering Joos Vandewalle

18. Current developments Next step in the development: • Bring plant operation / automation to higher level of autonomy • Monitor plant performance and detect changes on-line • Generate probing signals when necessary and based on economic considerations (least costly experiments) • Re-identify models and retune controllers on-line • Keep high performance control • Use economic performance criteria Autonomous economic model-basedoperation ofindustrial process systems Emeritaatsviering Joos Vandewalle

19. Thank you for your attention Emeritaatsviering Joos Vandewalle